Even in a "neural rendering optimistic" future, I can't see a way in which traditional rendering doesn't survive as a "control channel" that informs what the neural rendering does. To do otherwise would require making the bulk of the game logic neural too.
ofc, getting performance is the real problem. I like neural rendering ideas in the sense DLSS can make certain things happen on older machines that were definitely out of scope for deferred pipeline on same machine.
Its like a Boost u really dont want to use if u dont need it.
It might become that CGI will he the place for very accurate rendering more than games, but i sure hope not. (they can easily afford accuracy as they dont need realtime).
games need to slow down a notch on getting better graphics and go for a stabilization and expectation management pass. so people dont expect things that are only feasible through neural rendering techniques. It forces a lot of players to use it. like most AI. fomo or something...
Neural-assisted rendering tech is awesome because it actually exploits the spatial/temporal redundancy of the image stream to save render compute. There are very few techniques that can do it, and none I know can match the neural quality.
Frame 2 is highly redundant if you already have Frame 1 rendered, and even more so if you can supply the motion vectors. If you already have Frame 3 too? It becomes extremely redundant.
And yet, a conventional rendering pipeline would spend as much computation on it as it would on Frame 1. There is no "just reuse the previous work for cheap" primitive in conventional rendering. Every frame has to be built step after step, with the full depth of the rendering pipeline.
The same is true for neural upscaling. Going from 1080p to 4K means pushing 4x the pixels - and for a conventional renderer, that's nearly 4x the work. But a neural upscaling pipeline can exploit the redundancy of image data - and "fill in" the missing detail from a "ground truth" 1080p render, in a way that flies below the radar of human perception. Using cheaper neural operators instead of the full pipeline for it.
Neural rendering is a welcome optimization to conventional rendering techniques, in my eyes. As long as it's implemented right. Which is hard, but not impossible - we've come a long way already.
Also, I think "hard generative AI" is more useful in CGI than in real time rendering. Because real time rendering with user input demands a degree of repeatability, but CGI only has to "look good once". So you can accept the lowered accuracy of a largely untethered generative process with very few keyframes, and pick the "better" (more accurate, if that's what you want) outputs out of it.
This is great until the redundancy no longer exists and you have to rebuild the entire state machine from zero (a humble scene transition or rapidly turning a corner).
Gbuffers have been using motion vectors forever. For realtime global illumination, techniques like ReSTIR already allow for temporal reuse and spatial coherence.
I just don't see the purpose of replacing the traditional rendering pipeline for neural rendering techniques. It would be one thing if we were constrained on the number of triangles we could push per second, but we're not. The bottleneck is usually elsewhere in the pipeline: animating characters/objects, particle systems, physics updates, etc.
But the line is drawn when it involves CUDA and any part of their closed source compilers (nvcc).
There are obvious reasons why they are closed source, but it’s becoming pointless since Deepseek have open sourced their AI compiler and compute libraries with DeepGEMM and eventually they will catch up.
At least their support helps the open-weight ecosystem.
They care when the agendas align, and they don't when they won't.